{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-02</td>\n",
       "      <td>778.200012</td>\n",
       "      <td>781.650024</td>\n",
       "      <td>763.450012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>1872400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-03</td>\n",
       "      <td>767.250000</td>\n",
       "      <td>769.950012</td>\n",
       "      <td>759.030029</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>1943200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>750.659973</td>\n",
       "      <td>770.359985</td>\n",
       "      <td>750.560974</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>2134800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>774.500000</td>\n",
       "      <td>785.190002</td>\n",
       "      <td>772.549988</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>1585100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>783.400024</td>\n",
       "      <td>795.632996</td>\n",
       "      <td>780.190002</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>1350800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2016-11-02  778.200012  781.650024  763.450012  768.700012  768.700012   \n",
       "1  2016-11-03  767.250000  769.950012  759.030029  762.130005  762.130005   \n",
       "2  2016-11-04  750.659973  770.359985  750.560974  762.020020  762.020020   \n",
       "3  2016-11-07  774.500000  785.190002  772.549988  782.520020  782.520020   \n",
       "4  2016-11-08  783.400024  795.632996  780.190002  790.510010  790.510010   \n",
       "\n",
       "    Volume  \n",
       "0  1872400  \n",
       "1  1943200  \n",
       "2  2134800  \n",
       "3  1585100  \n",
       "4  1350800  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = 'D:/Jupyter/Project01/dataset/rlData.csv'\n",
    "data = pd.read_csv(filepath)\n",
    "data = data.sort_values('Date')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 7)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.figure(figsize = (15,9))\n",
    "plt.plot(data[['Close']])\n",
    "plt.xticks(range(0,data.shape[0],20), data['Date'].loc[::20], rotation=45)\n",
    "plt.title(\"****** Stock Price\",fontsize=18, fontweight='bold')\n",
    "plt.xlabel('Date',fontsize=18)\n",
    "plt.ylabel('Close Price (USD)',fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>768.700012</td>\n",
       "      <td>763.450012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>762.130005</td>\n",
       "      <td>759.030029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>762.020020</td>\n",
       "      <td>750.560974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>782.520020</td>\n",
       "      <td>772.549988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>790.510010</td>\n",
       "      <td>780.190002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>972.559998</td>\n",
       "      <td>972.200012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1019.270020</td>\n",
       "      <td>1008.200012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1017.109985</td>\n",
       "      <td>1007.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1016.640015</td>\n",
       "      <td>1010.419983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1025.500000</td>\n",
       "      <td>1016.950012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Close          Low\n",
       "0     768.700012   763.450012\n",
       "1     762.130005   759.030029\n",
       "2     762.020020   750.560974\n",
       "3     782.520020   772.549988\n",
       "4     790.510010   780.190002\n",
       "..           ...          ...\n",
       "247   972.559998   972.200012\n",
       "248  1019.270020  1008.200012\n",
       "249  1017.109985  1007.500000\n",
       "250  1016.640015  1010.419983\n",
       "251  1025.500000  1016.950012\n",
       "\n",
       "[252 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = data[['Close', 'Low']]\n",
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\application\\Anaconda3\\envs\\torch\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "price['Close'] = scaler.fit_transform(price['Close'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\application\\Anaconda3\\envs\\torch\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.774584</td>\n",
       "      <td>-0.751840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.819985</td>\n",
       "      <td>-0.782385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.820745</td>\n",
       "      <td>-0.840911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.679082</td>\n",
       "      <td>-0.688953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.623868</td>\n",
       "      <td>-0.636156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.634165</td>\n",
       "      <td>0.690750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.956949</td>\n",
       "      <td>0.939532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0.942022</td>\n",
       "      <td>0.934695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.938774</td>\n",
       "      <td>0.954874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Close       Low\n",
       "0   -0.774584 -0.751840\n",
       "1   -0.819985 -0.782385\n",
       "2   -0.820745 -0.840911\n",
       "3   -0.679082 -0.688953\n",
       "4   -0.623868 -0.636156\n",
       "..        ...       ...\n",
       "247  0.634165  0.690750\n",
       "248  0.956949  0.939532\n",
       "249  0.942022  0.934695\n",
       "250  0.938774  0.954874\n",
       "251  1.000000  1.000000\n",
       "\n",
       "[252 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price['Low'] = scaler.fit_transform(price['Low'].values.reshape(-1,1))\n",
    "price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 数据集制作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(stock, lookback):\n",
    "    data_raw = stock.to_numpy() \n",
    "    data = []\n",
    "    \n",
    "    # you can free play（seq_length）\n",
    "    for index in range(len(data_raw) - lookback): \n",
    "        data.append(data_raw[index: index + lookback])\n",
    "    \n",
    "    data = np.array(data);\n",
    "    test_set_size = int(np.round(0.2 * data.shape[0]))\n",
    "    train_set_size = data.shape[0] - (test_set_size)\n",
    "    \n",
    "    x_train = data[:train_set_size,:-1,:]\n",
    "    y_train = data[:train_set_size,-1,0:1]\n",
    "    \n",
    "    x_test = data[train_set_size:,:-1,:]\n",
    "    y_test = data[train_set_size:,-1,0:1]\n",
    "    \n",
    "    return [x_train, y_train, x_test, y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape =  (186, 19, 2)\n",
      "y_train.shape =  (186, 1)\n",
      "x_test.shape =  (46, 19, 2)\n",
      "y_test.shape =  (46, 1)\n"
     ]
    }
   ],
   "source": [
    "lookback = 20\n",
    "x_train, y_train, x_test, y_test = split_data(price, lookback)\n",
    "print('x_train.shape = ',x_train.shape)\n",
    "print('y_train.shape = ',y_train.shape)\n",
    "print('x_test.shape = ',x_test.shape)\n",
    "print('y_test.shape = ',y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：pytorch的nn.LSTM input shape=(seq_length, batch_size, input_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 模型构建 —— LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "x_train = torch.from_numpy(x_train).type(torch.Tensor)\n",
    "x_test = torch.from_numpy(x_test).type(torch.Tensor)\n",
    "y_train_lstm = torch.from_numpy(y_train).type(torch.Tensor)\n",
    "y_test_lstm = torch.from_numpy(y_test).type(torch.Tensor)\n",
    "y_train_gru = torch.from_numpy(y_train).type(torch.Tensor)\n",
    "y_test_gru = torch.from_numpy(y_test).type(torch.Tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = 2\n",
    "hidden_dim = 32\n",
    "num_layers = 2\n",
    "output_dim = 1\n",
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
    "        super(LSTM, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.num_layers = num_layers\n",
    "        \n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
    "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
    "        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
    "        out = self.fc(out[:, -1, :]) \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)\n",
    "criterion = torch.nn.MSELoss()\n",
    "optimiser = torch.optim.Adam(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  0 MSE:  0.21208934485912323\n",
      "Epoch  1 MSE:  0.18678613007068634\n",
      "Epoch  2 MSE:  0.15365348756313324\n",
      "Epoch  3 MSE:  0.09229738265275955\n",
      "Epoch  4 MSE:  0.023515749722719193\n",
      "Epoch  5 MSE:  0.1569804549217224\n",
      "Epoch  6 MSE:  0.05302024260163307\n",
      "Epoch  7 MSE:  0.01600123755633831\n",
      "Epoch  8 MSE:  0.03231644630432129\n",
      "Epoch  9 MSE:  0.05049539729952812\n",
      "Epoch  10 MSE:  0.057694293558597565\n",
      "Epoch  11 MSE:  0.05522824078798294\n",
      "Epoch  12 MSE:  0.04634707048535347\n",
      "Epoch  13 MSE:  0.03488298878073692\n",
      "Epoch  14 MSE:  0.025173772126436234\n",
      "Epoch  15 MSE:  0.019130492582917213\n",
      "Epoch  16 MSE:  0.01525820977985859\n",
      "Epoch  17 MSE:  0.01617373712360859\n",
      "Epoch  18 MSE:  0.022838527336716652\n",
      "Epoch  19 MSE:  0.02506636455655098\n",
      "Epoch  20 MSE:  0.022025587037205696\n",
      "Epoch  21 MSE:  0.018796389922499657\n",
      "Epoch  22 MSE:  0.015556950122117996\n",
      "Epoch  23 MSE:  0.01251350436359644\n",
      "Epoch  24 MSE:  0.011405347846448421\n",
      "Epoch  25 MSE:  0.012338635511696339\n",
      "Epoch  26 MSE:  0.013963061384856701\n",
      "Epoch  27 MSE:  0.0151028111577034\n",
      "Epoch  28 MSE:  0.01538014318794012\n",
      "Epoch  29 MSE:  0.014957875944674015\n",
      "Epoch  30 MSE:  0.014137635007500648\n",
      "Epoch  31 MSE:  0.013153416104614735\n",
      "Epoch  32 MSE:  0.012180286459624767\n",
      "Epoch  33 MSE:  0.011425384320318699\n",
      "Epoch  34 MSE:  0.011117041110992432\n",
      "Epoch  35 MSE:  0.011325579136610031\n",
      "Epoch  36 MSE:  0.011801889166235924\n",
      "Epoch  37 MSE:  0.012110192328691483\n",
      "Epoch  38 MSE:  0.011997979134321213\n",
      "Epoch  39 MSE:  0.011570996604859829\n",
      "Epoch  40 MSE:  0.01107844803482294\n",
      "Epoch  41 MSE:  0.010650279000401497\n",
      "Epoch  42 MSE:  0.010312470607459545\n",
      "Epoch  43 MSE:  0.010121017694473267\n",
      "Epoch  44 MSE:  0.010131584480404854\n",
      "Epoch  45 MSE:  0.010281914845108986\n",
      "Epoch  46 MSE:  0.01041125413030386\n",
      "Epoch  47 MSE:  0.010400944389402866\n",
      "Epoch  48 MSE:  0.010256078094244003\n",
      "Epoch  49 MSE:  0.010052753612399101\n",
      "Epoch  50 MSE:  0.009844285435974598\n",
      "Epoch  51 MSE:  0.00964594166725874\n",
      "Epoch  52 MSE:  0.009485770016908646\n",
      "Epoch  53 MSE:  0.009406575001776218\n",
      "Epoch  54 MSE:  0.009400641545653343\n",
      "Epoch  55 MSE:  0.009394402615725994\n",
      "Epoch  56 MSE:  0.00932509545236826\n",
      "Epoch  57 MSE:  0.009197420440614223\n",
      "Epoch  58 MSE:  0.009049606509506702\n",
      "Epoch  59 MSE:  0.008904128335416317\n",
      "Epoch  60 MSE:  0.008772960864007473\n",
      "Epoch  61 MSE:  0.008675338700413704\n",
      "Epoch  62 MSE:  0.008619942702353\n",
      "Epoch  63 MSE:  0.008584314957261086\n",
      "Epoch  64 MSE:  0.00853175763040781\n",
      "Epoch  65 MSE:  0.008444977924227715\n",
      "Epoch  66 MSE:  0.008335107937455177\n",
      "Epoch  67 MSE:  0.008222866803407669\n",
      "Epoch  68 MSE:  0.008121037855744362\n",
      "Epoch  69 MSE:  0.008034487254917622\n",
      "Epoch  70 MSE:  0.007964116521179676\n",
      "Epoch  71 MSE:  0.007902465760707855\n",
      "Epoch  72 MSE:  0.007833258248865604\n",
      "Epoch  73 MSE:  0.0077454037964344025\n",
      "Epoch  74 MSE:  0.007644964382052422\n",
      "Epoch  75 MSE:  0.007547334302216768\n",
      "Epoch  76 MSE:  0.007461097091436386\n",
      "Epoch  77 MSE:  0.007384379860013723\n",
      "Epoch  78 MSE:  0.007311906665563583\n",
      "Epoch  79 MSE:  0.007238567341119051\n",
      "Epoch  80 MSE:  0.007159164175391197\n",
      "Epoch  81 MSE:  0.007072036620229483\n",
      "Epoch  82 MSE:  0.006982825696468353\n",
      "Epoch  83 MSE:  0.006899635307490826\n",
      "Epoch  84 MSE:  0.006824188400059938\n",
      "Epoch  85 MSE:  0.006751311477273703\n",
      "Epoch  86 MSE:  0.006676031742244959\n",
      "Epoch  87 MSE:  0.006597115192562342\n",
      "Epoch  88 MSE:  0.006515477783977985\n",
      "Epoch  89 MSE:  0.006433728151023388\n",
      "Epoch  90 MSE:  0.0063555482774972916\n",
      "Epoch  91 MSE:  0.006281753536313772\n",
      "Epoch  92 MSE:  0.0062086740508675575\n",
      "Epoch  93 MSE:  0.006132790353149176\n",
      "Epoch  94 MSE:  0.006054657511413097\n",
      "Epoch  95 MSE:  0.005976908840239048\n",
      "Epoch  96 MSE:  0.0059011876583099365\n",
      "Epoch  97 MSE:  0.005827821791172028\n",
      "Epoch  98 MSE:  0.0057557434774935246\n",
      "Epoch  99 MSE:  0.005682765506207943\n",
      "Training time: 6.070207118988037\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "hist = np.zeros(num_epochs)\n",
    "start_time = time.time()\n",
    "lstm = []\n",
    "\n",
    "for t in range(num_epochs):\n",
    "    y_train_pred = model(x_train)\n",
    "\n",
    "    loss = criterion(y_train_pred, y_train_lstm)\n",
    "    print(\"Epoch \", t, \"MSE: \", loss.item())\n",
    "    hist[t] = loss.item()\n",
    "\n",
    "    optimiser.zero_grad()\n",
    "    loss.backward()\n",
    "    optimiser.step()\n",
    "    \n",
    "training_time = time.time()-start_time\n",
    "print(\"Training time: {}\".format(training_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 模型结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))\n",
    "original = pd.DataFrame(scaler.inverse_transform(y_train_lstm.detach().numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")    \n",
    "\n",
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.2, wspace=0.2)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "ax = sns.lineplot(x = original.index, y = original[0], label=\"Data\", color='royalblue')\n",
    "ax = sns.lineplot(x = predict.index, y = predict[0], label=\"Training Prediction (LSTM)\", color='tomato')\n",
    "ax.set_title('Stock price', size = 14, fontweight='bold')\n",
    "ax.set_xlabel(\"Days\", size = 14)\n",
    "ax.set_ylabel(\"Cost (USD)\", size = 14)\n",
    "ax.set_xticklabels('', size=10)\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "ax = sns.lineplot(data=hist, color='royalblue')\n",
    "ax.set_xlabel(\"Epoch\", size = 14)\n",
    "ax.set_ylabel(\"Loss\", size = 14)\n",
    "ax.set_title(\"Training Loss\", size = 14, fontweight='bold')\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 模型验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 10.91 RMSE\n",
      "Test Score: 15.49 RMSE\n"
     ]
    }
   ],
   "source": [
    "import math, time\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# make predictions\n",
    "y_test_pred = model(x_test)\n",
    "\n",
    "# invert predictions\n",
    "y_train_pred = scaler.inverse_transform(y_train_pred.detach().numpy())\n",
    "y_train = scaler.inverse_transform(y_train_lstm.detach().numpy())\n",
    "y_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())\n",
    "y_test = scaler.inverse_transform(y_test_lstm.detach().numpy())\n",
    "\n",
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))\n",
    "lstm.append(trainScore)\n",
    "lstm.append(testScore)\n",
    "lstm.append(training_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意Price此时的维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.774584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.819985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.820745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.679082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.623868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.634165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.956949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0.942022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.938774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Close\n",
       "0   -0.774584\n",
       "1   -0.819985\n",
       "2   -0.820745\n",
       "3   -0.679082\n",
       "4   -0.623868\n",
       "..        ...\n",
       "247  0.634165\n",
       "248  0.956949\n",
       "249  0.942022\n",
       "250  0.938774\n",
       "251  1.000000\n",
       "\n",
       "[252 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 注意这里标签索引是一个列表\n",
    "new_price = price[['Close']]\n",
    "new_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(new_price)\n",
    "trainPredictPlot[:, 0] = np.nan\n",
    "trainPredictPlot[lookback:len(y_train_pred)+lookback, :] = y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(new_price)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(y_train_pred)+lookback-1:len(price)-1, :] = y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "original = scaler.inverse_transform(price['Close'].values.reshape(-1,1))\n",
    "\n",
    "predictions = np.append(trainPredictPlot, testPredictPlot, axis=1)\n",
    "predictions = np.append(predictions, original, axis=1)\n",
    "result = pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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(USD)\"},\"tickfont\":{\"family\":\"Rockwell\",\"size\":12,\"color\":\"white\"},\"showline\":true,\"showgrid\":true,\"showticklabels\":true,\"linecolor\":\"white\",\"linewidth\":2,\"ticks\":\"outside\"},\"showlegend\":true,\"annotations\":[{\"font\":{\"color\":\"white\",\"family\":\"Rockwell\",\"size\":26},\"showarrow\":false,\"text\":\"Results (LSTM)\",\"x\":0.0,\"xanchor\":\"left\",\"xref\":\"paper\",\"y\":1.05,\"yanchor\":\"bottom\",\"yref\":\"paper\"}]},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('9cf8990f-cb05-4a02-8247-df4fdc355146');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[0],\n",
    "                    mode='lines',\n",
    "                    name='Train prediction')))\n",
    "fig.add_trace(go.Scatter(x=result.index, y=result[1],\n",
    "                    mode='lines',\n",
    "                    name='Test prediction'))\n",
    "fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[2],\n",
    "                    mode='lines',\n",
    "                    name='Actual Value')))\n",
    "fig.update_layout(\n",
    "    xaxis=dict(\n",
    "        showline=True,\n",
    "        showgrid=True,\n",
    "        showticklabels=False,\n",
    "        linecolor='white',\n",
    "        linewidth=2\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title_text='Close (USD)',\n",
    "        titlefont=dict(\n",
    "            family='Rockwell',\n",
    "            size=12,\n",
    "            color='white',\n",
    "        ),\n",
    "        showline=True,\n",
    "        showgrid=True,\n",
    "        showticklabels=True,\n",
    "        linecolor='white',\n",
    "        linewidth=2,\n",
    "        ticks='outside',\n",
    "        tickfont=dict(\n",
    "            family='Rockwell',\n",
    "            size=12,\n",
    "            color='white',\n",
    "        ),\n",
    "    ),\n",
    "    showlegend=True,\n",
    "    template = 'plotly_dark'\n",
    "\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "annotations = []\n",
    "annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
    "                              xanchor='left', yanchor='bottom',\n",
    "                              text='Results (LSTM)',\n",
    "                              font=dict(family='Rockwell',\n",
    "                                        size=26,\n",
    "                                        color='white'),\n",
    "                              showarrow=False))\n",
    "fig.update_layout(annotations=annotations)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建新特征 --> 滞后"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
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 "nbformat_minor": 4
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